Application US20190133480

DISCRETIZED EMBEDDINGS OF PHYSIOLOGICAL WAVEFORMS
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US20190133480

1. A method implemented at least in part by one or more processors, comprising: (8) (12)

obtaining a first continuous stream of samples measured by one or more physiological sensors;

discretizing the first continuous stream of samples to generate a training sequence of quantized beats;

determining a training sequence of vectors corresponding to the training sequence of quantized beats, wherein each vector of the training sequence of vectors is determined based on a respective quantized beat of the training sequence of quantized beats and an embedding matrix;

associating a label with each vector of the training sequence of vectors, wherein each label is indicative of a medical condition that is evidenced by samples of the first continuous stream of samples obtained during a time interval associated with the respective vector of the training sequence of vectors;

applying the training sequence of vectors as input across a neural network to generate corresponding instances of training output;

comparing each instance of training output to the label that is associated with the corresponding vector of the training sequence of vectors;

based on the comparing, training the neural network and the embedding matrix;

obtaining a second continuous stream of samples from one or more of the physiological sensors;

discretizing the second continuous stream of samples to generate a live sequence of quantized beats;

determining a live sequence of vectors corresponding to the live sequence of quantized beats, wherein each vector of the live sequence of vectors is determined based on a respective quantized beat and the embedding matrix;

applying the live sequence of vectors as input across the neural network to generate corresponding instances of live output; and

providing, at one or more output devices operably coupled with one or more of the processors, information indicative of the live output.

2. The method of claim 1, wherein discretizing the first continuous stream of samples includes: (0) (3)

3. The method of claim 1, wherein the first and second continuous streams of samples comprises electrocardiogram data. (1) (0)

5. The method of claim 1, wherein one or both of the first and second continuous streams of samples are discretized at one or more of the physiological sensors, and one or both of the training sequence of quantized beats and the live sequence of quantized beats are provided by one or more of the physiological sensors to the one or more processors. (0)

6. The method of claim 1, wherein one or both of the first and second continuous streams of samples are discretized using one or more additional neural networks. (0)

7. The method of claim 1, wherein the neural network comprises a recurrent neural network. (1) (0)

9. The method of claim 1, wherein training the neural network includes applying back propagation with stochastic gradient descent. (0)

10. The method of claim 1, wherein training the embedding matrix includes determining weights of the embedding matrix. (0)

11. The method of claim 1, further comprising: (0) (2)

12. A system comprising one or more processors and memory operably coupled with the one or more processors, wherein the memory stores instructions that, in response to execution of the instructions by one or more processors, cause the one or more processors to perform the following operations: (6) (12)